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ABSTRACT We present the most comprehensive catalogue to date of Type I superluminous supernovae (SLSNe), a class of stripped-envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of ultraviolet, optical, and infrared photometry totalling over 30 000 photometric detections. Using these data, we derive observational parameters such as the peak absolute magnitudes, rise and decline time-scales, as well as bolometric luminosities, temperature, and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of $$^{56}$$Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at $$\approx 6.5$$ M$$_\odot$$. We find no significant redshift dependence for any parameter, and no evidence for distinct subtypes of SLSNe. We find that only a small fraction of SLSNe, $$\lt 3$$ per cent, are best fit with a significant radioactive decay component $$\gtrsim 50$$ per cent. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works.
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Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification
Abstract With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only ∼0.1% of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium Deep Survey (PS1-MDS), classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a subsample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multiband light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients ( MOSFiT ). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are consistent overall, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower-luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way for an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.
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- Award ID(s):
- 2019786
- PAR ID:
- 10376758
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 937
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3847/1538-4357/ac87ff
